Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "158"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 158 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 26 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 26 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 158, Node N12:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2460009 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.523832 -0.403199 -0.316396 -0.223322 1.444618 2.262692 3.062418 15.352998 0.5978 0.6198 0.3783 nan nan
2460008 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.284193 -0.511516 -0.284983 -0.338498 1.229621 1.552819 1.651801 4.898170 0.6403 0.6655 0.3326 nan nan
2460007 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.426463 -0.484681 0.078900 0.156228 1.733002 1.708277 4.642664 20.139160 0.6035 0.6239 0.3600 nan nan
2459999 digital_ok 0.00% 99.00% 99.08% 0.00% - - nan nan nan nan nan nan nan nan 0.3701 0.3501 0.1587 nan nan
2459998 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.309583 -0.100576 -0.009757 -0.025716 2.638483 2.252499 7.545724 23.861341 0.5972 0.6204 0.3869 nan nan
2459997 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.231701 -0.212429 -0.127952 0.030396 1.482885 1.563633 10.169574 35.341389 0.6083 0.6319 0.3898 nan nan
2459996 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.858675 -0.207435 0.069057 0.051847 1.227788 1.618141 4.641976 14.199375 0.6202 0.6432 0.3980 nan nan
2459995 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.131523 -0.366376 -0.276970 -0.249892 2.610575 2.447770 3.806120 13.310867 0.6031 0.6278 0.3973 nan nan
2459994 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.419119 -0.153836 -0.040662 0.085213 2.017724 1.725953 3.382096 14.055231 0.5970 0.6190 0.3930 nan nan
2459993 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.317765 0.085705 -0.229774 -0.053906 1.794031 1.691214 2.788600 14.310071 0.5725 0.6168 0.4201 nan nan
2459991 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.148351 -0.343237 -0.021729 0.004108 1.494072 1.868758 2.429534 13.323817 0.6122 0.6293 0.3911 nan nan
2459990 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.014144 -0.275433 0.059458 -0.017134 1.776275 1.689617 1.862020 16.521502 0.6088 0.6278 0.3884 nan nan
2459989 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.045561 -0.268218 0.037350 0.210333 1.795348 1.847512 1.541627 14.715351 0.6027 0.6236 0.3897 nan nan
2459988 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.196212 -0.276786 -0.031438 -0.167991 1.741006 1.427237 1.911680 12.044159 0.6042 0.6268 0.3831 nan nan
2459987 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.221112 -0.620194 -0.126913 -0.004073 1.451307 1.358559 2.983701 18.096814 0.6134 0.6326 0.3796 nan nan
2459986 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.256789 -0.394441 -0.109823 -0.158678 2.066225 1.541393 1.858147 8.628635 0.6308 0.6549 0.3423 nan nan
2459985 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.545979 -0.637559 -0.187222 -0.740931 1.622766 1.348372 4.266819 15.019246 0.6138 0.6345 0.3899 nan nan
2459984 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.272726 -0.500275 -0.344963 -0.849345 -0.225249 0.584866 2.401017 7.932037 0.6280 0.6503 0.3695 nan nan
2459983 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.220468 -0.340354 -0.032218 -0.579039 1.295977 1.600119 2.085018 8.680578 0.6440 0.6767 0.3203 nan nan
2459982 digital_ok 0.00% 0.00% 0.00% 0.00% - - -0.151842 -0.786885 0.217310 -0.146990 1.385965 1.557168 0.346234 1.037962 0.6830 0.6941 0.2983 nan nan
2459981 digital_ok 100.00% 0.00% 0.00% 0.00% - - -0.014352 0.001049 -0.065096 -0.578811 1.392260 2.105142 1.401236 12.480849 0.6116 0.6362 0.3897 nan nan
2459980 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.298671 -0.083663 -0.207358 -0.463861 1.720477 1.778477 0.424189 2.595176 0.6511 0.6709 0.3173 nan nan
2459979 digital_ok 100.00% 0.00% 0.00% 0.00% - - -0.453224 0.011352 -0.216738 -0.413406 1.087095 1.946019 1.511003 11.661172 0.6048 0.6326 0.3916 nan nan
2459978 digital_ok 100.00% 0.00% 0.00% 0.00% - - -0.105680 0.277137 -0.207753 -0.510703 1.452996 2.176122 2.420484 15.056672 0.6055 0.6317 0.3989 nan nan
2459977 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.259339 0.057067 -0.247409 -0.503175 1.678916 1.708494 3.022250 15.667127 0.5703 0.5974 0.3570 nan nan
2459976 digital_ok 100.00% 0.00% 0.00% 0.00% - - 0.063627 0.072036 -0.193947 -0.531984 2.314157 2.664355 2.162524 12.633363 0.6108 0.6365 0.3925 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 158: 2460009

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 15.352998 0.523832 -0.403199 -0.316396 -0.223322 1.444618 2.262692 3.062418 15.352998

Antenna 158: 2460008

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 4.898170 -0.511516 0.284193 -0.338498 -0.284983 1.552819 1.229621 4.898170 1.651801

Antenna 158: 2460007

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 20.139160 0.426463 -0.484681 0.078900 0.156228 1.733002 1.708277 4.642664 20.139160

Antenna 158: 2459999

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Shape nan nan nan nan nan nan nan nan nan

Antenna 158: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 23.861341 0.309583 -0.100576 -0.009757 -0.025716 2.638483 2.252499 7.545724 23.861341

Antenna 158: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 35.341389 0.231701 -0.212429 -0.127952 0.030396 1.482885 1.563633 10.169574 35.341389

Antenna 158: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 14.199375 0.858675 -0.207435 0.069057 0.051847 1.227788 1.618141 4.641976 14.199375

Antenna 158: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 13.310867 0.131523 -0.366376 -0.276970 -0.249892 2.610575 2.447770 3.806120 13.310867

Antenna 158: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 14.055231 0.419119 -0.153836 -0.040662 0.085213 2.017724 1.725953 3.382096 14.055231

Antenna 158: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 14.310071 0.317765 0.085705 -0.229774 -0.053906 1.794031 1.691214 2.788600 14.310071

Antenna 158: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 13.323817 0.148351 -0.343237 -0.021729 0.004108 1.494072 1.868758 2.429534 13.323817

Antenna 158: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 16.521502 -0.275433 0.014144 -0.017134 0.059458 1.689617 1.776275 16.521502 1.862020

Antenna 158: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 14.715351 -0.268218 0.045561 0.210333 0.037350 1.847512 1.795348 14.715351 1.541627

Antenna 158: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 12.044159 -0.276786 0.196212 -0.167991 -0.031438 1.427237 1.741006 12.044159 1.911680

Antenna 158: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 18.096814 0.221112 -0.620194 -0.126913 -0.004073 1.451307 1.358559 2.983701 18.096814

Antenna 158: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 8.628635 -0.394441 0.256789 -0.158678 -0.109823 1.541393 2.066225 8.628635 1.858147

Antenna 158: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 15.019246 -0.637559 0.545979 -0.740931 -0.187222 1.348372 1.622766 15.019246 4.266819

Antenna 158: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 7.932037 0.272726 -0.500275 -0.344963 -0.849345 -0.225249 0.584866 2.401017 7.932037

Antenna 158: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 8.680578 0.220468 -0.340354 -0.032218 -0.579039 1.295977 1.600119 2.085018 8.680578

Antenna 158: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Variability 1.557168 -0.151842 -0.786885 0.217310 -0.146990 1.385965 1.557168 0.346234 1.037962

Antenna 158: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 12.480849 0.001049 -0.014352 -0.578811 -0.065096 2.105142 1.392260 12.480849 1.401236

Antenna 158: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 2.595176 -0.083663 0.298671 -0.463861 -0.207358 1.778477 1.720477 2.595176 0.424189

Antenna 158: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 11.661172 -0.453224 0.011352 -0.216738 -0.413406 1.087095 1.946019 1.511003 11.661172

Antenna 158: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 15.056672 0.277137 -0.105680 -0.510703 -0.207753 2.176122 1.452996 15.056672 2.420484

Antenna 158: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 15.667127 0.259339 0.057067 -0.247409 -0.503175 1.678916 1.708494 3.022250 15.667127

Antenna 158: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
158 N12 digital_ok nn Temporal Discontinuties 12.633363 0.072036 0.063627 -0.531984 -0.193947 2.664355 2.314157 12.633363 2.162524
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